1. Field of the Invention
The subject matter of the present invention generally involves spectral image analysis. More particularly, the present invention involves processes for determining representative scene components, or endmembers, from spectral imagery.
2. Description of the Related Art
It has long been recognized that at spatial scales typical of remotely sensed data, the surface corresponding to a given pixel is rarely composed of a single physical component, but rather a combination of xe2x80x9csub-pixelxe2x80x9d constituents. This mixed-pixel concept was developed into a quantitative model for interpretation of spectral images, Spectral Mixture Analysis (xe2x80x9cSMAxe2x80x9d). SMA involves an interactive selection and refinement of key components, or endmembers, to achieve an acceptable solution. The SMA model is based on the assumption that remotely sensed spectral measurements are mixed signatures, which vary across the scene as the relative proportion of each component changes. SMA can therefore be used to translate spectral imagery into quantitative maps also referred to as abundance maps, showing the percent cover of each spectrally distinct scene component. The number of endmembers resolvable with SMA is dependent on the complexity of the actual surface, the spatial resolution of the imagery, and the spectral resolution of the data. However, even in hyperspectral imagery of complex regions, SMA is most robust when a few ( less than 10) spectrally distinct components are modeled. For instance, a forested scene would be modeled as a combination of green vegetation (e.g. leaves, grass), non-photosynthetic vegetation (NPV, e.g. bark, wood, dry leaves), soils, and shade. This last component, shade, is unique to SMA. The shade endmember accounts for spectral variations in illumination caused by either topography or sub-pixel surface texture. With SMA, a tree or group of trees can therefore be modeled not by a single xe2x80x9ctreexe2x80x9d signature, but instead by a combination of green vegetation, NPV, and shade. The selection of these types of endmembers generally requires at least one expert user""s input to be successful.
Alternatively, complex statistical and mathematical approaches have been employed in order to perform endmember selection, but none have been shown to provide routine and reliable results. These approaches utilize purely mathematical transformations of the spectral data, without regard for what physical information the data actually represents, e.g., vegetation, NPV, soil, shade, buildings, roads, etc. The endmembers determined through these processes are physically meaningless and not comparable to those derived by expert users. As such, their utility is limited and at best require subsequent subjective interpretation by an expert.
Current endmember selection methods are thus either manual with expert users doing the processing or automatic using signal processing, but with less reliable results that still require expert user interpretation.
Currently, the process of determining the number and nature of these spectrally distinct components (or xe2x80x9cendmembersxe2x80x9d) using either the manual or statistical selection process is both subjective and labor intensive. Because of this, SMA-based classification, although clearly of significant use in many applications, has largely been limited to labor intensive research projects. In such studies, endmembers are typically selected manually by experienced analysts. Results from this process are subjective and vary from one investigator to another. Further, as the complexity of data sets increase, so does the time required to perform SMA as does the likelihood of greater error. Consequently, there is a need in the art for a faster, more objective, more cost-effective, user-friendly process for selecting representative spectral endmembers for a scene. As more and more data become available from, e.g., commercial airborne systems and earth observational satellites which include hyperspectral sensors, there is an increasing need for a process that aids both experienced and novice end-users to work more efficiently, significantly reduces costs, and enhances the utility of spectral imagery.
The invention described herein is an objective and automated process for determining spectral endmembers and transforming Spectral Mixture Analysis (SMA) from a widely used research technique into a user-friendly tool that can support the needs of all types of remote sensing. The process extracts endmembers from a spectral dataset using a knowledge-based approach. The process identifies a series of starting spectra that are consistent with a scene and its environment. The process then finds endmembers iteratively, selecting each new endmember based on a combination of physically and statistically-based tests. The tests combine spectral and spatial criteria and decision trees to ensure that the resulting endmembers are representative of the scene.
The invention described herein achieves a significant advancement in the application of SMA. As discussed herein, the process of the present invention successfully implements a number of the manual decision making steps that an analyst performs in doing mixture analysis. However, these steps are ordered in a way that leads to far fewer false steps, fewer refinements of endmember selection, and, minimizes the need to remove entire endmember sets from an analysis sequence. Further, the process of the present invention provides users with sophisticated insight into mixture model methods. This is particularly important when working with, for example, complex scenes or on areas where specific objectives must be met.
As a result of the processes described herein, the time required to exploit spectral image data is dramatically reduced. The process makes information from spectral imagery readily available, and expedites critical decision making. The process automatically determines the subpixel proportions of materials in spectral datasets and transforms these data into readily interpretable information layers that can be imported into, e.g., a geographical information system (GIS) database.
Applications of spectral imaging data can support a diverse range of scientific and business operations. Among these, spectral data offers enormous potential for routinely creating geologic and soil maps, environmental monitoring, aiding forestry and agriculture production, land use/land cover mapping, and various applications in support of civil, military, law enforcement, and intelligence communities. For example, high resolution spectral imaging is used to help winegrowers harvest their crops with more precision. By identifying plant health and maturity, vintners can establish optimum harvest schedules that ultimately yield high quality wine. The same methodology holds true for using spectral imaging data to non-destructively detect bruised fruit in large orchards, and also aiding law enforcement officials in detecting marijuana plants from airborne platforms.
More particularly, an embodiment of the present invention provides a process for determining at least one candidate spectral endmember that represents a group of N spectra. The process of this embodiment comprises: building an initial endmember set composed of at least a first and a second spectrum representing at least a first and a second spectral characteristic expected to be in the group of N spectra; unmixing the N spectra within the group of N spectra to determine what portion, if any, of each of the N spectra match at least one of the at least a first and second spectrum; calculating a first metric value for each of the N spectra, wherein the first metric value accounts for a remaining portion of each of the N spectra not matching the at least a first and second spectrum; defining a metric value range, wherein the N spectra having first metric values within the metric value range are defined as M spectra; ordering the M spectra from highest first metric value to lowest first metric value; comparing each of the M spectra, beginning with the M spectra having the highest first metric value, to each of the N spectra, to determine the frequency with which each of the M spectra occurs within the N spectra; and calculating a second metric value for each of the M spectra, wherein the second metric value combines the frequency of occurrence of each of the M spectra within the N spectra with a first metric value for each of the M spectra, wherein the M spectra having the largest second metric value is the at least one candidate endmember.
A further embodiment of the present invention provides a process for determining at least one candidate spectral endmember that represents a group of N spectra. The process of this embodiment comprises: building an initial endmember set composed of at least a first and a second spectrum representing at least a first and a second spectral characteristic expected to be in the group of N spectra; unmixing the N spectra within the group of spectra to determine what portion, if any, of each of the N spectra match at least one of the at least a first and a second spectrum; defining an error value for each of the N spectra, wherein the error value is the portion of each of the N spectra that does not match a combination of the at least a first and a second spectrum; comparing the error value for each of the N spectra to a predetermined error value range, wherein spectra having error values within the predetermined error value range are defined as M spectra; ordering the M spectra from highest error value to lowest error value; comparing each of the M spectra, beginning with the M spectra having the highest error value, to each of the N spectra, to determine the frequency with which each of the M spectra occurs within the N spectra; and calculating a metric for each of the M spectra, wherein the metric combines the frequency of occurrence of each of the M spectra within the N spectra with an error value for each of the M spectra, wherein the M spectra having the largest metric is the at least one candidate endmember.
A still further embodiment of the present invention provides a process for determining at least one candidate spectral endmember within a scene having N pixels using scene spectral data. This embodiment of the present invention comprises: building a first endmember, wherein the first endmember represents a first spectral characteristic expected to be in the scene; building a second endmember for the scene, wherein the second endmember represents a second spectral characteristic expected to be in the scene; unmixing the N pixels in the scene to determine what portions of each of the N pixels match at least one of the first and second endmembers; defining a remaining portion for each of the N pixels not matching a combination of the first and second endmembers as an error value, wherein each error value corresponds to a residual spectra and the residual spectra in combination form a residual spectrum of the N pixels; calculating a root mean square (RMS) error for the N pixels by combining the error values for the residual spectra; determining an acceptable range of deviation from the mean RMS error; comparing each of the RMS error values for each of the N pixels to the acceptable range of deviation from the mean RMS error and keeping the M pixels that are within the acceptable range of deviation; ordering the M pixels from highest RMS error value to lowest RMS error value; comparing the corresponding residual spectra of the M pixels, beginning with the M pixel having the highest RMS error value, to the residual spectrum comprising the residual spectrum for the N pixels, to determine the frequency with which each of the corresponding residual spectra of the M pixels occurs within the residual spectra for the N pixels; and calculating a weighting factor for each of the M pixels, wherein the M pixel having the largest weighting factor is the at least one candidate endmember.